This comprehensive and uniquely organized text is aimed at undergraduate and graduate level statistics courses in education, psychology, and other social sciences. A conceptual approach, built around common issues and problems rather than statistical techniques, allows students to understand the conceptual nature of statistical procedures and to focus more on cases and examples of analysis. Wherever possible, presentations contain explanations of the underlying reasons behind a technique. Importantly, this is one of the first statistics texts in the social sciences using R as the principal statistical package. Key features include the following.
- Conceptual Focus – The focus throughout is more on conceptual understanding and attainment of statistical literacy and thinking than on learning a set of tools and procedures.
- Problems and Cases – Chapters and sections open with examples of situations related to the forthcoming issues, and major sections ends with a case study. For example, after the section on describing relationships between variables, there is a worked case that demonstrates the analyses, presents computer output, and leads the student through an interpretation of that output.
- Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text.
- Companion Website – A companion website contains instructions on how to use R, SAS, and SPSS to solve the end-of-chapter exercises and offers additional exercises.
- Field Tested – The manuscript has been field tested for three years at two leading institutions.
Table of Contents
1. Introduction and Background II. Descriptive Statistics 2. Describing Quantitative Data with Frequency Distributions 3. Describing Quantitative Data: Summary Statistics 4. Describing Categorical Data: Frequency Distributions, Graphics, and Summary Statistics 5. Describing the Position of a Case within a Set of Scores 6. Describing the Relationship between Two Quantitative Variables: Correlation 7. Describing the Relationship between Two Quantitative Variables: Regression III. The Fundamentals of Statistical Inference 8. The Essentials of Probability 9. Probability and Sampling Distributions 10. The Normal Distribution IV. Statistical Inference 11. The Basics of Statistical Inference: Tests of Location 12. Other One-Sample Tests for Location 13. More One-Sample Tests 14. Two-Sample Tests of Location 15. Other Two-Sample Tests: Variability and Relationships V. K-Sample Tests 16. Tests on Location: Analysis of Variance and Other Selected Procedures 17. Multiple Comparison Procedures 18. Looking Back… and Beyond Appendix A: Statistical Tables Appendix B: Getting Started with R Index
William B. Ware is Professor at the University of North Carolina at Chapel Hill, USA.
John M. Ferron is Professor at the University of South Florida, USA.
Barbara M. Miller is Associate Professor at Elon University, USA.
Please visit our companion website for additional support materials.